用3D框信息提升单目目标运动估计精度，适合微型无人机应用
Observability-Enhanced Target Motion Estimation via Bearing-Box: Theory and MAV Applications
- 利用3D边界框内含信息，不依赖目标形状和运动方向假设
- 在真实场景中实现更优的运动与尺寸联合估计性能
- 特别适用于微型无人机，无需高阶运动假设
单目视觉目标运动估计在众多应用中面临基础挑战。本文提出一种新型的方位框（bearing-box）方法，充分挖掘当前广泛可用但尚未被充分探索的3D检测信息。与依赖各向同性目标形状和横向运动等限制性假设的现有方法不同，本方法通过解析3D边界框中的隐含信息，无需这些假设即可同时估计目标运动状态和物理尺寸。当应用于多旋翼微型飞行器（MAVs）时，该估计算法进一步利用飞行器加速度与推力间的独特耦合关系，消除了对高阶运动假设的需求。这具有重要意义，因为高阶运动假设在最先进的基于方位的估计算法中普遍被视为必要条件。研究通过严格的可观测性分析和广泛的实验验证支持了上述结论，在真实场景中展示了优越的性能。
原文摘要 · Abstract (English)
Monocular vision-based target motion estimation is a fundamental challenge in numerous applications. This work introduces a novel bearing-box approach that fully leverages modern 3D detection measurements that are widely available nowadays but have not been well explored for motion estimation so far. Unlike existing methods that rely on restrictive assumptions such as isotropic target shape and lateral motion, our bearing-box estimator can estimate both the target's motion and its physical size without these assumptions by exploiting the information buried in a 3D bounding box. When applied to multi-rotor micro aerial vehicles (MAVs), the estimator yields an interesting advantage: it further removes the need for higher-order motion assumptions by exploiting the unique coupling between MAV's acceleration and thrust. This is particularly significant, as higher-order motion assumptions are widely believed to be necessary in state-of-the-art bearing-based estimators. We support our claims with rigorous observability analyses and extensive experimental validation, demonstrating the estimator's superior performance in real-world scenarios.
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